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Haydebug

roblox-analytics-mcp

by Haydebug

get_server_status

Verify MCP server configuration: API key availability and origin, default universe, saved aliases, query budget. On authentication errors, distinguish a missing key from one lacking analytics scope.

Instructions

Check how this MCP server is configured: whether an API key is available and where it came from, the default universe, saved aliases, and the query budget. Call this first if an analytics tool returns an authentication error — it distinguishes a missing key from a key that lacks the analytics scope.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. The verb 'Check' plus the enumerated read-only configuration fields strongly imply a non-mutating diagnostic operation. It does not explicitly state the output format or whether it makes any external calls, but for a simple status tool the behavioral intent is clear enough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact, front-loaded with the core purpose, and every sentence adds value: the first defines scope, the second provides actionable diagnostic usage guidance. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters, no output schema, and a clearly bounded diagnostic purpose, the description is complete. It covers what is checked, when to use it, and why, which is sufficient for an AI agent to select and invoke the tool appropriately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and the schema coverage is 100%, so the baseline is 4. The description appropriately describes the output fields rather than parameters, making clear what the status check will reveal.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Check') and resource ('how this MCP server is configured'), then enumerates the exact configuration aspects covered: API key availability/provenance, default universe, saved aliases, and query budget. This clearly distinguishes it from sibling analytics and universe tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives an explicit when-to-use directive: 'Call this first if an analytics tool returns an authentication error', and explains why it is useful ('distinguishes a missing key from a key that lacks the analytics scope'). This is strong, actionable usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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